TREEasy: An automated workflow to infer gene trees,species trees,and phylogenetic networks from multilocus data

Multilocus genomic data sets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user‐friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python—TREEasy—that performs automated sequence alignment (with MAFFT), gene tree inference (with IQ‐Tree), species inference from concatenated data (with IQ‐Tree and RaxML‐NG), species tree inference from gene trees (with ASTRAL, MP‐EST, and STELLS2), and phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires FASTA files and nine parameters as inputs. The tool can be run as command line or through a Graphical User Interface (GUI). As examples, we reproduced a recent analysis of staghorn coral evolution, and performed a new analysis on the evolution of the “WGD clade” of yeast. The latter revealed novel patterns that were not identified by previous analyses. TREEasy represents a reliable and simple tool to accelerate research in systematic biology ( https://github.com/MaoYafei/TREEasy ).     

全国大学生生命科学竞赛数据分析与展望

全国大学生生命科学竞赛至今已举办三届,赛事组织好、规模大、参与度高,对促进生命科学教育与研究具有重要作用。文中简述全国大学生生命科学竞赛的模式与现状,并基于前三届竞赛数据分地区分年度统计分析报名数据和竞赛成绩,同时结合生命科学领域的新变化新认识进行展望,以更好地推动赛事发展。     

Cannot see the diversity for all the species: Evaluating inclusion criteria for local species lists when using abundant citizen science data

Abundant citizen science data on species occurrences are becoming increasingly available and enable identifying composition of communities occurring at multiple sites with high temporal resolution. However, for species displaying temporary patterns of local occurrences that are transient to some sites, biodiversity measures are clearly dependent on the criteria used to include species into local species lists. Using abundant opportunistic citizen science data from frequently visited wetlands, we investigated the sensitivity of α‐ and β‐diversity estimates to the use raw versus detection‐corrected data and to the use of inclusion criteria for species presence reflecting alternative site use. We tested seven inclusion criteria (with varying number of days required to be present) on time series of daily occurrence status during a breeding season of 90 days for 77 wetland bird species. We show that even when opportunistic presence‐only observation data are abundant, raw data may not produce reliable local species richness estimates and rank sites very differently in terms of species richness. Furthermore, occupancy model based α‐ and β‐diversity estimates were sensitive to the inclusion criteria used. Total species lists (all species observed at least once during a season) may therefore mask diversity differences among sites in local communities of species, by including vagrant species on potentially breeding communities and change the relative rank order of sites in terms of species richness. Very high sampling effort does not necessarily free opportunistic data from its inherent bias and can produce a pattern in which many species are observed at least once almost everywhere, thus leading to a possible paradox: The large amount of biological information may hinder its usefulness. Therefore, when prioritizing among sites to manage or preserve species diversity estimates need to be carefully related to relevant inclusion criteria depending on the diversity estimate in focus.     

Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood‐dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large‐scale occupancies of species with differing landscape‐scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization–extinction models, conducting the modeling at alternative spatial scales and using fine‐ or coarse‐resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization–extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization–extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization–extinction models over occupancy models, modeling the process at the finest resource‐unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.     

Dynamic response in the larval geoduck (Panopea generosa) proteome to elevated pCO2

Pacific geoducks (Panopea generosa) are clams found along the northeast Pacific coast where they are important components of coastal and estuarine ecosystems and a major aquaculture product. The Pacific coastline, however, is also experiencing rapidly changing ocean habitat, including significant reductions in pH. To better understand the physiological impact of ocean acidification on geoduck clams, we characterized for the first time the proteomic profile of this bivalve during larval development and compared it to that of larvae exposed to low pH conditions. Geoduck larvae were reared at pH 7.5 (ambient) or pH 7.1 in a commercial shellfish hatchery from day 6 to day 19 postfertilization and sampled at six time points for an in‐depth proteomics analysis using high‐resolution data‐dependent analysis. Larvae reared at low pH were smaller than those reared at ambient pH, especially in the prodissoconch II phase of development, and displayed a delay in their competency for settlement. Proteomic profiles revealed that metabolic, cell cycle, and protein turnover pathways differed between the two pH and suggested that differing phenotypic outcomes between pH 7.5 and 7.1 are likely due to environmental disruptions to the timing of physiological events. In summary, ocean acidification results in elevated energetic demand on geoduck larvae, resulting in delayed development and disruptions to normal molecular developmental pathways, such as carbohydrate metabolism, cell growth, and protein synthesis.     

基于植保大数据的病虫害移动智能采集新设备

为提高农作物重大病虫害发生信息自动化、智能化采集能力,全面提升监测预警水平,笔者基于大数据、人工智能和深度学习技术,研发了一款农作物病虫害移动智能采集设备——智宝,主要实现了3个方面的功能:一是病虫害发生信息自动采集上报.通过该产品进行人工拍照,可实现对田间农作物重大病虫害发生图像、发生位置、发生数量、微环境因子等数据的实时采集和上报.二是自动识别计数.基于植保大数据与人工智能技术,通过构建病虫害自动识别系统,可实现重大病虫害精准识别与分析,只要拍摄照片,即可快速、精确地识别病虫害种类,并自动计数、上报到指定的测报系统.三是自动分析判别分级.针对拍摄采集上报的重大病虫害发生信息,系统可在自动识别和计数的基础上,进一步对病虫害发生严重程度进行智能判别分级,甚至根据相关预测模型,对病虫害的发生趋势进行辅助分析预测,提出预测建议.通过2016—2019年组织多地植保机构进行试验改进,该技术产品日趋成熟,有望在未来的农作物病虫害发生信息采集和预测预报工作中推广使用.     

Length-weight relationship of fish species captured around an artificial offshore reef (northern Rio de Janeiro,Brazil)

The present work provides length-weight relationships (LWRs) for fish species captured around artificial offshore reef deployed 5 km from Guaxindiba Port, northern Rio de Janeiro, Brazil. The fish were captured during two sampling periods each year between 1996 and 2017 using bottom gill nets (25 m_length × 3 m_depth; 30 mm mesh). The fish were kept on ice and the biometric data, including total length (cm) and total wet weight (g) were determined in the laboratory. A total of 16 species belonging to 10 families were analyzed with Sciaenidae being the species richest family (5 spp) in the samples. The new value of LWRs for Sphyraena guachancho and new maximum sizes recorded for seven species highlight the scarcity of information on biological aspects of South America coastal fishes. These LWRs should assist fisheries scientist and managers to complement their further studies of population parameters to improve management decisions.